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Probability & Statistics for Machine Learning & Data Science
  • Level Foundation
  • المدة 29 ساعات hours
  • الطبع بواسطة DeepLearning.AI
  • Offered by

عن

Mathematics for Machine Learning and Data science is a foundational online program created in by DeepLearning.AI and taught by Luis Serrano. This beginner-friendly program is where you’ll master the fundamental mathematics toolkit of machine learning. After completing this course, learners will be able to: • Describe and quantify the uncertainty inherent in predictions made by machine learning models, using the concepts of probability, random variables, and probability distributions. • Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science like Bernoulli, Binomial, and Gaussian distributions • Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems • Assess the performance of machine learning models using interval estimates and margin of errors • Apply concepts of statistical hypothesis testing to commonly used tests in data science like AB testing Many machine learning engineers and data scientists struggle with mathematics. Challenging interview questions often hold people back from leveling up in their careers, and even experienced practitioners can feel held by a lack of math skills.  This specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow plugins and visualizations to help you see how the math behind machine learning actually works. Upon completion, you’ll understand the mathematics behind all the most common algorithms and data analysis techniques — plus the know-how to incorporate them into your machine learning career.

الوحدات

Lesson 1 - Introduction to Probability

    • 17 Videos
    • 4 Readings
    • 2 Lab
    • 1 Assignment
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1 Assignment

  • Week 1 - Practice Quiz

2 Labs

  • Four Birthday Problems
  • Monty Hall Problem

17 Videos

  • Course Introduction
  • A note on programming experience
  • What is Probability?
  • What is Probability? - Dice Example
  • Complement of Probability
  • Sum of Probabilities (Disjoint Events)
  • Sum of Probabilities (Joint Events)
  • Independence
  • Birthday problem
  • Conditional Probability - Part 1
  • Conditional Probability - Part 2
  • Bayes Theorem - Intuition
  • Bayes Theorem - Mathematical Formula
  • Bayes Theorem - Spam example
  • Bayes Theorem - Prior and Posterior
  • Bayes Theorem - The Naive Bayes Model
  • Probability in Machine Learning

4 Readings

  • [IMPORTANT] Have questions, issues or ideas? Join our Forum!
  • Check your knowledge
  • Learning Python: Recommended Resources
  • Interactive Tool: Repeated Experiments

Lesson 2 - Probability Distributions

    • 12 Videos
    • 1 Readings
    • 2 Lab
    • 1 Assignment
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1 Assignment

  • Week 1 - Summative quiz

2 Labs

  • Exploratory Data Analysis - Intro to Pandas
  • Exploratory Data Analysis - Exploring Your Data

12 Videos

  • Random Variables
  • Probability Distributions (Discrete)
  • Binomial Distribution
  • (Optional) Binomial Coefficient
  • Bernoulli Distribution
  • Probability Distributions (Continuous)
  • Probability Density Function
  • Cumulative Distribution Function
  • Uniform Distribution
  • Normal Distribution
  • (Optional) Chi-Squared Distribution
  • Sampling from a Distribution

1 Readings

  • Interactive Tool: Relationship between PMF/PDF and CDF of some distributions

Programming Assignment - Probability Distributions

    • 3 Readings
    • 1 Programming
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1 Programming

  • Naive Bayes

3 Readings

  • (Optional) Common Coursera Labs Operations
  • (Optional) Assignment Troubleshooting Tips
  • (Optional) Partial Grading for Assignments

Week 1 Wrap Up

    • 1 Videos
    • 1 Readings
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1 Videos

  • Week 1 - Conclusion

1 Readings

  • Week 1 - Slides

Lesson 1 - Describing Distributions

    • 16 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Week 2 - Practice Quiz

16 Videos

  • Expected Value
  • Other measures of central tendency: median and mode
  • Expected value of a Function
  • Sum of expectations
  • Variance
  • Standard Deviation
  • Sum of Gaussians
  • Standardizing a Distribution
  • Skewness and Kurtosis: Moments of a Distribution
  • Skewness and Kurtosis - Skewness
  • Skewness and Kurtosis - Kurtosis
  • Quantiles and Box-Plots
  • Visualizing data: Box-Plots
  • Visualizing data: Kernel density estimation
  • Visualizing data: Violin Plots
  • Visualizing data: QQ plots

1 Readings

  • Interactive Tool: Mean, median and standard deviation

Lesson 2 - Probability Distributions with Multiple Variables

    • 10 Videos
    • 2 Lab
    • 1 Assignment
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1 Assignment

  • Week 2 - Summative Quiz

2 Labs

  • Summary statistics and visualization of data sets
  • Exploratory Data Analysis - Data Visualization and Summary Statistics

10 Videos

  • Joint Distribution (Discrete) - Part 1
  • Joint Distribution (Discrete) - Part 2
  • Joint Distribution (Continuous)
  • Marginal and Conditional Distribution
  • Conditional Distribution
  • Covariance of a Dataset
  • Covariance of a Probability Distribution
  • Covariance Matrix
  • Correlation Coefficient
  • Multivariate Gaussian Distribution

Programming Assignment - Loaded Dice

    • 1 Lab
    • 1 Programming
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1 Programming

  • Loaded Dice

1 Labs

  • Simulating Dice Rolls with Numpy (helper for the assignment, not necessary and not graded)

Week 2 Wrap Up

    • 1 Videos
    • 1 Readings
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1 Videos

  • Week 2 - Conclusion

1 Readings

  • Week 2 - Slides

Lesson 1 - Population and Sample

    • 7 Videos
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Week 3 - Practice Quiz

1 Labs

  • Sampling data from different distribution and studying the distribution of sample mean

7 Videos

  • Population and Sample
  • Sample Mean
  • Sample Proportion
  • Sample Variance
  • Law of Large Numbers
  • Central Limit Theorem - Discrete Random Variable
  • Central Limit Theorem - Continuous Random Variable

Lesson 2 - Point Estimation

    • 12 Videos
    • 2 Readings
    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Week 3 - Summative Quiz

1 Labs

  • Exploratory Data Analysis - Linear Regression

12 Videos

  • Point Estimation
  • Maximum Likelihood Estimation: Motivation
  • MLE: Bernoulli Example
  • MLE: Gaussian Example
  • MLE: Linear Regression
  • Regularization
  • Back to "Bayesics"
  • Bayesian Statistics - Frequentist vs. Bayesian
  • Bayesian Statistics - MAP
  • Bayesian Statistics - Updating Priors
  • Bayesian Statistics - Full Worked Example
  • Relationship between MAP, MLE and Regularization

2 Readings

  • MLE for Gaussian population
  • Interactive Tool: Likelihood Functions

Week 3 Wrap Up

    • 1 Videos
    • 1 Readings
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1 Videos

  • Week 3 - Conclusion

1 Readings

  • Week 3 - Slides

Lesson 1 - Confidence Intervals

    • 9 Videos
    • 1 Readings
    • 1 Assignment
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1 Assignment

  • Week 4 - Practice Quiz

9 Videos

  • Confidence Intervals - Overview
  • Confidence Intervals - Changing the Interval
  • Confidence Intervals - Margin of Error
  • Confidence Intervals - Calculation Steps
  • Confidence Intervals - Example
  • Calculating Sample Size
  • Difference Between Confidence and Probability
  • Unknown Standard Deviation
  • Confidence Intervals for Proportion

1 Readings

  • Interactive Tool: Confidence Intervals

Lesson 2 - Hypothesis Testing

    • 12 Videos
    • 2 Readings
    • 1 Lab
    • 1 Assignment
Show info about module content

1 Assignment

  • Week 4 - Summative Quiz

1 Labs

  • Exploratory Data Analysis - Confidence Intervals and Hypothesis Testing

12 Videos

  • Defining Hypotheses
  • Type I and Type II errors
  • Right-Tailed, Left-Tailed, and Two-Tailed Tests
  • p-Value
  • Critical Values
  • Power of a Test
  • Interpreting Results
  • t-Distribution
  • t-Tests
  • Two Sample t-Test
  • Paired t-Test
  • ML Application: A/B Testing

2 Readings

  • Test for proportions
  • Two sample test for proportions

End of access to Lab Notebooks

    • 1 Readings
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1 Readings

  • [IMPORTANT] Reminder about end of access to Lab Notebooks

Programming Assignment - AB Testing

    • 1 Programming
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1 Programming

  • A/B Testing

Week 4 Wrap Up

    • 1 Videos
    • 1 Readings
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1 Videos

  • Week 4 - Conclusion

1 Readings

  • Week 4 - Slides

Acknowledgments & Course Resources

    • 3 Readings
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3 Readings

  • Acknowledgments
  • (Optional) Opportunity to Mentor Other Learners
  • References

Auto Summary

"Probability & Statistics for Machine Learning & Data Science" is a foundational online course by DeepLearning.AI, taught by Luis Serrano. Ideal for beginners, it focuses on mastering the essential mathematics toolkit for machine learning. Covering probability, random variables, common distributions, statistical methods, and hypothesis testing, the course includes innovative pedagogy and visualizations for intuitive learning. Available on Coursera with a 1740-minute duration, this course is perfect for aspiring and experienced data scientists and machine learning engineers looking to strengthen their math skills. Subscription options include a Starter plan.

Luis Serrano
Instructor

Luis Serrano